| Country | agricultural_land_p_2016 | food_index_2015 | forest_area_p_2015 |
|---|---|---|---|
| Costa Rica | 34.45946 | 126.09 | 53.97571 |
| Italy | 43.23451 | 93.40 | 31.60740 |
| Chile | 21.17165 | 111.84 | 23.85237 |
| Angola | 47.47734 | 181.06 | 46.40732 |
| Cyprus | 12.15693 | 80.83 | 18.69048 |
| Country | population_growth_p_2015 | aded_val_GDP_2015 |
|---|---|---|
| Costa Rica | 1.0869528 | 4.956675 |
| Italy | -0.0963761 | 2.065237 |
| Chile | 1.1777575 | 3.638883 |
| Angola | 3.4388507 | 9.122535 |
| Cyprus | 0.7521855 | 1.874272 |
| n | min | median | mean | max | sd |
|---|---|---|---|---|---|
| 184 | 0.5576923 | 39.95677 | 38.88085 | 82.55971 | 21.85271 |
Figure 1. Distribution for the percent of agricultural land in different countries, in 2016
Figure 2. Distribution for the 2015 food production index for different countries
Figure 7.1. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against their 2015 food production index. The red line is the best fit line. The blue curve is the Loess curve.
Figure 3. Distribution for the percent of forest area in different countries, in 2015
Figure 7.1. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against their percent of forest area, in 2015. The red line is the best fit line. The blue curve is the Loess curve.
Figure 3. Distribution for the percent annual population growth for different countries in 2015.
Figure 7.1. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against their percent annual population growth in 2015. The red line is the best fit line. The blue curve is the Loess curve.
Figure 3. Distribution for the Added value of Agriculture, forestry, and fishing to the GDP of different countries, in 2015
Figure 7.1. Interactive Scatterplot for the percent of agricultural land in different countries, in 2016 against the added value of Agriculture, forestry, and fishing to their GDP in 2015. The red line is the best fit line. The blue curve is the Loess curve.
##
## Call:
## lm(formula = agricultural_land_p_2016 ~ ns(food_index_2015, df = 4) +
## ns(population_growth_p_2015, df = 4) + forest_area_p_2015 +
## ns(aded_val_GDP_2015, df = 4), data = tidy_joined_dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.655 -11.874 -0.442 13.314 39.414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.68221 19.34390 1.121 0.263919
## ns(food_index_2015, df = 4)1 19.45436 13.08654 1.487 0.138975
## ns(food_index_2015, df = 4)2 14.41294 10.28967 1.401 0.163121
## ns(food_index_2015, df = 4)3 28.95063 30.29454 0.956 0.340612
## ns(food_index_2015, df = 4)4 -3.06774 11.46043 -0.268 0.789269
## ns(population_growth_p_2015, df = 4)1 -6.22456 11.12223 -0.560 0.576455
## ns(population_growth_p_2015, df = 4)2 7.96660 10.34965 0.770 0.442519
## ns(population_growth_p_2015, df = 4)3 -35.10175 25.60437 -1.371 0.172205
## ns(population_growth_p_2015, df = 4)4 -51.68574 14.33979 -3.604 0.000411 ***
## forest_area_p_2015 -0.40591 0.06205 -6.542 6.86e-10 ***
## ns(aded_val_GDP_2015, df = 4)1 17.02739 6.18359 2.754 0.006535 **
## ns(aded_val_GDP_2015, df = 4)2 12.50304 8.91819 1.402 0.162747
## ns(aded_val_GDP_2015, df = 4)3 47.21576 14.67902 3.217 0.001553 **
## ns(aded_val_GDP_2015, df = 4)4 12.00965 14.59038 0.823 0.411592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.77 on 170 degrees of freedom
## Multiple R-squared: 0.3144, Adjusted R-squared: 0.262
## F-statistic: 5.996 on 13 and 170 DF, p-value: 4.052e-09
Figure 14. Normal Q-Qplot for the percent of agricultural land in different countries, in 2016
Figure 15. Residuals distribution for the statistical model
Figure 16. Residuals graph for the fitted values, with a Lowess curve in blue and a horizontal line at zero in red.
Figure 17. Residuals graph for the food production Index, with a Lowess curve in blue and a horizontal line at zero in red.
Figure 18. Residuals graph for the percent of forest area in different countries, in 2015, with a Lowess curve in blue and a horizontal line at zero in red.
Figure 18. Residuals graph for the percent annual population growth for different countries in 2015, with a Lowess curve in blue and a horizontal line at zero in red.
Figure 18. Residuals graph for the Added value of Agriculture, forestry, and fishing to the GDP of different countries, in 2015, with a Lowess curve in blue and a horizontal line at zero in red.
| GVIF | Df | GVIF^(1/(2*Df)) | |
|---|---|---|---|
| ns(food_index_2015, df = 4) | 1.616606 | 4 | 1.061880 |
| ns(population_growth_p_2015, df = 4) | 1.964128 | 4 | 1.088043 |
| forest_area_p_2015 | 1.100039 | 1 | 1.048827 |
| ns(aded_val_GDP_2015, df = 4) | 2.018545 | 4 | 1.091767 |
## lm(formula = agricultural_land_p_2016 ~ ns(food_index_2015, df = 4) +
## ns(population_growth_p_2015, df = 4) + forest_area_p_2015 +
## ns(aded_val_GDP_2015, df = 4), data = tidy_joined_dataset)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 21.68221 | 19.34390 | 1.12088 | 0.26392 |
| ns(food_index_2015, df = 4)1 | 19.45436 | 13.08654 | 1.48659 | 0.13897 |
| ns(food_index_2015, df = 4)2 | 14.41294 | 10.28967 | 1.40072 | 0.16312 |
| ns(food_index_2015, df = 4)3 | 28.95063 | 30.29454 | 0.95564 | 0.34061 |
| ns(food_index_2015, df = 4)4 | -3.06774 | 11.46043 | -0.26768 | 0.78927 |
| ns(population_growth_p_2015, df = 4)1 | -6.22456 | 11.12223 | -0.55965 | 0.57645 |
| ns(population_growth_p_2015, df = 4)2 | 7.96660 | 10.34965 | 0.76975 | 0.44252 |
| ns(population_growth_p_2015, df = 4)3 | -35.10175 | 25.60437 | -1.37093 | 0.17220 |
| ns(population_growth_p_2015, df = 4)4 | -51.68574 | 14.33979 | -3.60436 | 0.00041 |
| forest_area_p_2015 | -0.40591 | 0.06205 | -6.54190 | 0.00000 |
| ns(aded_val_GDP_2015, df = 4)1 | 17.02739 | 6.18359 | 2.75364 | 0.00653 |
| ns(aded_val_GDP_2015, df = 4)2 | 12.50304 | 8.91819 | 1.40197 | 0.16275 |
| ns(aded_val_GDP_2015, df = 4)3 | 47.21576 | 14.67902 | 3.21655 | 0.00155 |
| ns(aded_val_GDP_2015, df = 4)4 | 12.00965 | 14.59038 | 0.82312 | 0.41159 |
| Value | df | |
|---|---|---|
| Residual Standard Error | 18.773 | 170 |
| Multiple R-squared | 0.314 | |
| Adjusted R-squared | 0.262 |
| Value | Numerator df | Denominator df | |
|---|---|---|---|
| Model F-statistic | 5.996 | 13 | 170 |
| P-value | 4.052e-09 |
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| ns(food_index_2015, df = 4) | 4 | 4401.079 | 1100.2697 | 3.1218 | 0.0165 |
| ns(population_growth_p_2015, df = 4) | 4 | 4768.515 | 1192.1286 | 3.3825 | 0.0108 |
| forest_area_p_2015 | 1 | 13639.881 | 13639.8812 | 38.7009 | 0.0000 |
| ns(aded_val_GDP_2015, df = 4) | 4 | 4665.087 | 1166.2718 | 3.3091 | 0.0122 |
| Residuals | 170 | 59915.433 | 352.4437 | NA | NA |
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| ns(food_index_2015, df = 4) | 4 | 4401.079 | 1100.2697 | 3.121831 | 0.0164792 |
| ns(population_growth_p_2015, df = 4) | 4 | 4768.515 | 1192.1286 | 3.382465 | 0.0108242 |
| forest_area_p_2015 | 1 | 13639.881 | 13639.8812 | 38.700877 | 0.0000000 |
| ns(aded_val_GDP_2015, df = 4) | 4 | 4665.087 | 1166.2718 | 3.309101 | 0.0121861 |
| Residuals | 170 | 59915.433 | 352.4437 | NA | NA |